Zinc oxide-doped poly(urethane) spider web nanofibrous scaffold via one-step electrospinning: a novel matrix for tissue engineering

Zinc oxide (ZnO) nanostructures have been commonly studied for electronic purposes due to their unique piezoelectric and catalytic properties; however, recently, they have been also exploited for biomedical applications. The purpose of this study was to fabricate ZnO-doped poly(urethane) (PU) nanocomposite via one-step electrospinning technique. The utilized nanocomposite was prepared by using colloidal gel composed of ZnO and PU, and the obtained mats were vacuum dried at 60 °C overnight. The physicochemical characterization of as-spun composite nanofibers was carried out by X-ray diffraction pattern, field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, electron probe microanalysis, and transmission electron microscopy, whereas the thermal behavior was analyzed by thermogravimetric analysis. The viability, attachment, and proliferation of NIH 3T3 mouse fibroblast cells on the ZnO/PU composite nanofibers were analyzed by in vitro cell compatibility test. The morphological features of the cells attached on nanofibers were examined by Bio-SEM. We conclude that the electrospun nanofibrous scaffolds with unique spider nets had good biocompatibility. Cytotoxicity experiments indicated that the mouse fibroblasts could attach to the nanocomposite after being cultured. Thus, the current work demonstrates that the as-synthesized ZnO/PU hybrid nanofibers represent a promising biomaterial to be exploited for various tissue engineering applications.     

Application of LogitBoost Classifier for Traceability Using SNP Chip Data

Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.     

Genome-wide analysis of copy number variations reveals that aging processes influence body fat distribution in Korea Associated Resource (KARE) cohorts

Many anthropometric measures, including body mass index (BMI), waist-to-hip ratio (WHR), and subcutaneous fat thickness, are used as indicators of nutritional status, fertility and predictors of future health outcomes. While BMI is currently the best available estimate of body adiposity, WHR and skinfold thickness at various sites (biceps, triceps, suprailiac, and subscapular) are used as indices of body fat distribution. Copy number variation (CNV) is an attractive emerging approach to the study of associations with various diseases. In this study, we investigated the dosage effect of genes in the CNV genome widely associated with fat distribution phenotypes in large cohorts. We used the Affymetrix genome-wide human SNP Array 5.0 data of 8,842 healthy unrelated adults in KARE cohorts and identified CNVs associated with BMI and fat distribution-related traits including WHR and subcutaneous skinfold thickness at suprailiac (SUP) and subscapular (SUB) sites. CNV segmentation of each chromosome was performed using Golden Helix SVS 7.0, and single regression analysis was used to identify CNVs associated with each phenotype. We found one CNV for BMI, 287 for WHR, 2,157 for SUP, and 2,102 for SUB at the 5?% significance level after Holm–Bonferroni correction. Genes included in the CNV were used for the analysis of functional annotations using the Database for Annotation, Visualization and Integrated Discovery (DAVID v6.7b) tool. Functional gene classification analysis identified five significant gene clusters (metallothionein, ATP-binding proteins, ribosomal proteins, kinesin family members, and zinc finger proteins) for SUP, three (keratin-associated proteins, zinc finger proteins, keratins) for SUB, and one (protamines) for WHR. BMI was excluded from this analysis because the entire structure of no gene was identified in the CNV. Based on the analysis of genes enriched in the clusters, the fat distribution traits of KARE cohorts were related to the fat redistribution associated with the aging process. In addition to structural variation, dosage effect analysis of genes based on CNV is useful to gain an understanding of the comprehensive biological phenomena underlying particular phenotypes and/or diseases.     

Identification of endogenous microRNA references in porcine serum for quantitative real-time PCR normalization

MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs that regulate the expression of genes, and they affect important biological and physiological states. Circulating miRNAs in blood are useful markers of metabolism and economic traits. Expression levels of circulating miRNAs have been estimated using quantitative real-time PCR (qPCR). Proper normalization is critical for accurate miRNA expression analysis. However, there is no study which systematically presented endogenous reference genes for evaluating circulating miRNA expression in pigs. In this study, ten porcine miRNAs (let-7a, miR-16, miR-17, miR-23a, miR-26a, miR-93, miR-103, miR-107, miR-127 and miR-191), based on the literature, were chosen as candidate reference miRNAs in serum. We evaluated the expression stability value of these miRNAs in Berkshire, Duroc, Landrace and Yorkshire pigs using geNorm and NormFinder. We determined the optimal combination of reference miRNAs for qPCR experiments: miR-127 and miR-17 in Berkshire pigs; miR-127 and miR-93 in Duroc and Landrace pigs; miR-127 and miR-16 in Yorkshire pigs. miR-127 was the best reference gene in pigs, regardless of the breed. Our study is crucial for the discovery of novel biomarkers in pigs. The reference miRNAs presented in this study could be used as appropriate reference genes for the measurement of circulating miRNA levels in studies of physiological blood metabolites.

     

Pyrosequencing-based analysis of fecal microbial communities in three purebred pig lines

This study examined the fecal bacterial diversity of 15-weekold pigs from three purebred lines: Duroc, Landrace, and Yorkshire. Taxon-dependent and -independent analyses were performed to evaluate differences in the fecal bacterial communities and to identify bacterial genera that can be used to discriminate breeds, following high-throughput pyrosequencing of 16S rRNA genes. Among the breeds evaluated, Landrace had the most diverse bacterial community composition. Prevotella, Blautia, Oscillibacter, and Clostridium were detected in all samples regardless of breed. On the other hand, Catenibacterium, Blautia, Dialister, and Sphaerochaeta were differentially detected among breeds, as demonstrated by the canonical loading plot. The discriminant analysis of principal components plot also showed clear separation of the three purebred pig lines, with a certain degree of similarity between Landrace and Yorkshire pigs and a distinct separation between Duroc pigs and the other two breeds. Other factors not related to breed, such as season or time of sampling and pen effects, may contribute to shaping the gut microbiota of pigs.